Crafting the Perfect Media List: A Step-by-Step Guide

Crafting an effective media list can often feel like threading a needle. Our module is designed to provide you with a list of the top 30 media outlets, cherry-picked for their reach within any given category. However, we also understand that niche news sites, despite their lower reach, can hold special relevance for you. Here's a simplified guide on how to create a media list that melds the best of both worlds.


  1. Initiate the process by creating a media list for your category using the search function
  2. Download this list and carefully select the high-impact news domains you wish to retain.
  3. Now, navigate to the Upload section and enter these domains into the 'Publication URLs' section.
  4. In the same section (Publication URLs), add the niche domain names you wish to analyze.
  5. Finally, click 'Save and Create List' and get a media list tailored to your specific needs, combining the power of high-impact and niche domains.
  6. By following these steps, you blend high-impact news domains with your niche list, capturing an ideal balance.

P.S. If you require a more extensive media list beyond the limit of 30, don't hesitate to reach out to us via email at [email protected].

Beyond the Code: Tackling Algorithmic Bias

Let me start by saying that Algorithms can and are biased!

Algorithms have become deeply ingrained in our everyday existence, moulding how we absorb information, form judgments, and engage with our surroundings. Whether it's tailored suggestions or automated decision-making processes, algorithms hold the ability to impact our day-to-day encounters. Yet, as we grow more dependent on algorithms, a significant worry arises: the presence of algorithmic bias.

Algorithmic bias refers to the systematic errors or unfairness that can occur in algorithms, leading to unequal treatment or outcomes for certain groups of people. The consequences of algorithmic bias can be far-reaching, perpetuating social inequities and reinforcing existing biases in our society. As an industry leader in the field of technology and entrepreneurship, it is crucial to recognize the importance of tackling algorithmic bias head-on and proactively working towards fair and unbiased algorithms.

In this article, I will try delve into the complex issue of algorithmic bias, exploring its various forms, underlying causes, and the consequences of inaction. We will also discuss strategies for addressing algorithmic bias, including the role of diverse teams, data quality and evaluation, and the potential of algorithmic auditing. Furthermore, we will highlight Arkreach's approach to tackling algorithmic bias and share specific case studies or examples of bias detection and mitigation within our platform. Finally, we will advocate for collective responsibility in combating algorithmic bias and discuss the path to ethical algorithms.

What is Algorithmic Bias

To effectively address algorithmic bias, we must first understand its nature and impact. Algorithmic bias occurs when algorithms produce results that systematically favour or discriminate against certain individuals or groups. This bias can manifest in various forms, such as racial, gender, or socioeconomic bias. For example, a hiring algorithm that favours candidates from certain educational backgrounds may perpetuate socioeconomic disparities.

Real-world examples of algorithmic bias have garnered significant attention in recent years. In the criminal justice system, algorithms used for risk assessment have been found to disproportionately classify individuals from minority communities as high risk, leading to biased outcomes and perpetuating systemic injustices. In the realm of healthcare, algorithms used for diagnostics or treatment recommendations have been shown to exhibit racial biases, resulting in differential healthcare outcomes for different racial groups.

Detecting and addressing algorithmic bias can be challenging due to several factors. One major challenge is the lack of transparency and explainability in many algorithms. Complex machine learning models often operate as black boxes, making it difficult to understand how decisions are being made and identify the sources of bias. Additionally, biased data can inadvertently introduce bias into algorithms. If historical data contains societal biases or reflects systemic discrimination, algorithms trained on such data will likely reproduce those biases.

Unveiling the Causes

To effectively tackle algorithmic bias, we need to examine its underlying causes. One significant factor contributing to bias in algorithms is biased data. Algorithms learn patterns and make predictions based on the data they are trained on. If the training data contains inherent biases or reflects historical inequalities, the algorithm can inadvertently perpetuate those biases in its outcomes.

Biased data can arise from various sources, including societal biases, historical discrimination, and skewed data collection processes. For example, if historical hiring practices have favoured certain demographics, the data used to train a hiring algorithm may reflect those biases, leading to biased recommendations or the exclusion of qualified candidates from underrepresented groups.

However, biased data alone does not fully explain algorithmic bias. Human bias also plays a crucial role. Humans develop and train algorithms, and they can introduce their own biases consciously or unconsciously during the development process. Even with unbiased data, if the people involved in algorithm development hold biased beliefs or perspectives, those biases can seep into the algorithms themselves.

Removing subjectivity from algorithms is a significant challenge. While we strive for objective decision-making, algorithms are designed by humans and inevitably carry some degree of subjectivity. The challenge lies in identifying and addressing these biases, making algorithms more transparent and accountable.

Automated decision-making processes, while efficient, can also contribute to algorithmic bias. Relying solely on algorithms to make decisions without human oversight can lead to unintended consequences. Algorithms may lack the context, nuance, and ethical considerations that humans can bring to the decision-making process. Balancing the advantages of automation with the need for human judgment is crucial to mitigating algorithmic bias effectively.

The Consequences of Inaction

The consequences of unchecked algorithmic bias can be far-reaching, impacting various aspects of our lives. In critical domains such as healthcare, hiring, and criminal justice, biased algorithms can perpetuate systemic injustices and exacerbate existing disparities.

In healthcare, algorithms are increasingly being used for diagnostics, treatment recommendations, and patient triage. However, when these algorithms exhibit bias, certain patient populations may receive inadequate or delayed care. For example, if a diagnostic algorithm exhibits racial bias, it may result in misdiagnosis or delayed treatment for patients from marginalized communities.

In the hiring process, algorithms are often utilized to screen and shortlist candidates. However, if these algorithms are biased against certain demographics, it can lead to discriminatory practices and reinforce existing inequalities. Qualified candidates from underrepresented groups may be overlooked, perpetuating systemic disparities in employment opportunities.

The criminal justice system is another domain where the consequences of algorithmic bias are particularly concerning. Risk assessment algorithms used for bail, sentencing, and parole decisions have been found to disproportionately classify individuals from minority communities as high risk, leading to biased outcomes and perpetuating systemic injustices. The potential for biased algorithms to reinforce discriminatory practices and disproportionately impact marginalized communities is a significant ethical concern.

By allowing algorithmic bias to persist, we risk entrenching societal biases, deepening divisions, and hindering progress towards a more equitable society. It is imperative that we take proactive steps to address algorithmic bias and strive for fair and unbiased outcomes.

Strategies for Addressing Algorithmic Bias

Addressing algorithmic bias requires a multi-faceted approach that involves various strategies and considerations. By implementing these strategies, we can work towards developing fair and unbiased algorithms that contribute to a more equitable society.

One crucial approach to mitigating algorithmic bias is fostering diverse and inclusive teams in algorithm development. When individuals from diverse backgrounds and perspectives collaborate, they bring unique insights and challenge each other's assumptions, helping to identify and rectify biases. Diverse teams can better understand the potential impact of algorithms on different communities and strive for fairness and inclusivity in their designs.

Data quality and representativeness are also essential factors in combating algorithmic bias. It is crucial to ensure that the training data used for algorithms is comprehensive, representative, and free from biases. Careful attention should be given to data collection methods, validation processes, and ongoing evaluation to detect and rectify any biases that may arise.

Algorithmic auditing and transparency initiatives can play a significant role in addressing algorithmic bias. By conducting regular audits of algorithms, organizations can identify potential biases and take corrective actions. Transparency in algorithmic decision-making, such as providing explanations for algorithmic outcomes, can increase accountability and enable individuals to understand how algorithms affect their lives.

Additionally, ongoing evaluation and monitoring are necessary to ensure that algorithms remain fair and unbiased over time. Algorithms should be regularly tested and benchmarked against diverse datasets to identify and rectify any emerging biases. Continuous improvement and learning are crucial to maintaining ethical algorithms and staying ahead of potential biases.

Arkreach's Approach to Tackling Algorithmic Bias

At Arkreach, we recognize the importance of addressing algorithmic bias and strive to develop a platform that provides fair and unbiased insights. Our approach to tackling algorithmic bias encompasses several key principles.

Firstly, we prioritize diverse and inclusive teams in our algorithm development process. By bringing together individuals with different perspectives and backgrounds, we foster an environment that challenges biases and ensures a wide range of voices are represented.

Secondly, we place great emphasis on data quality and representativeness. We carefully curate our datasets, ensuring they are comprehensive, diverse, and free from biases. Rigorous validation processes and ongoing evaluation help us detect and rectify any biases that may arise, ensuring our algorithms provide equitable and unbiased insights.

User feedback plays a vital role in our approach to addressing algorithmic bias. We actively encourage our users to provide feedback on any potential biases they observe or concerns they may have. This feedback helps us identify and rectify biases, enabling us to continuously improve the fairness and accuracy of our platform.

To showcase our commitment to addressing algorithmic bias, we have implemented specific case studies and examples within Arkreach. These case studies highlight the detection and mitigation of bias within our algorithms, demonstrating our dedication to providing fair and unbiased insights to our users.

The Path to Ethical Algorithms

Addressing algorithmic bias requires a collective effort from industry leaders, organizations, and policymakers. To create a future of ethical algorithms, collaboration and a shared commitment to fairness and transparency are essential.

Industry-wide collaboration plays a crucial role in combating algorithmic bias. By sharing best practices, insights, and challenges, organizations can collectively work towards developing ethical guidelines and standards that promote fairness and transparency in algorithms. Open dialogue and knowledge exchange facilitate continuous learning and improvement in algorithmic fairness.

Regulatory measures and standards can also contribute to the path of ethical algorithms. Policymakers can play a vital role in creating frameworks that ensure accountability, transparency, and fairness in algorithmic decision-making. By implementing regulations and standards that address algorithmic bias, society can foster an environment where algorithms are developed and deployed responsibly.

Continued research, innovation, and improvement are fundamental to advancing algorithmic fairness. The field of algorithmic bias is rapidly evolving, and it is crucial to stay abreast of the latest developments and insights. Through ongoing research, collaboration, and a commitment to continuous improvement, we can strive for algorithms that are truly fair, transparent, and accountable.

In the end, tackling algorithmic bias is a critical imperative for creating a fair and inclusive future. As algorithms continue to shape our lives and make decisions that impact individuals and communities, it is our responsibility as industry leaders, organizations, and policymakers to ensure that these algorithms are free from bias and promote equitable outcomes.

However, the journey towards ethical algorithms is not without its challenges. Detecting and mitigating algorithmic bias requires vigilance, transparency, and collaboration. It necessitates a collective effort from researchers, practitioners, policymakers, and the wider society.

As an industry leader, it is incumbent upon us to champion ethical practices and advocate for algorithmic fairness. We need to commit ourselves to fostering diverse and inclusive teams, upholding high standards of data quality, conducting regular audits, and promoting transparency in algorithmic decision-making. Let us also engage in ongoing research, innovation, and collaboration to stay at the forefront of algorithmic fairness.

In closing, let us recognize the immense power of algorithms in shaping our world. By proactively addressing algorithmic bias, we can harness this power for good, creating a future where algorithms contribute to a fair and inclusive society. Let us seize the opportunity to shape the future and build a world where everyone can benefit equitably from the opportunities offered by algorithms.

The journey towards ethical algorithms is ongoing. It requires our collective commitment, continuous learning, and a steadfast dedication to fairness. Together, we can pave the way for a future where algorithms truly serve the best interests of all.

The AI First Approach: A New Blueprint for Entrepreneurs

Artificial intelligence (AI) is revolutionizing industries, reshaping economies, and altering the very fabric of our society[1]. As we stand on the cusp of a new era, I firmly believe that the future of entrepreneurship is AI-first. As an entrepreneur and technologist myself, I have experienced firsthand the transformative potential of AI. Through my journey with ArkReach, an AI-driven analytics tool for communication professionals, I have seen how embedding AI at the core of a business from its inception can lead to innovative solutions and successful outcomes[2].

In this article, I will explore the concept of AI-first entrepreneurship, the opportunities it presents, the challenges it poses, and how we can navigate them. Whether you're an aspiring entrepreneur, a seasoned business leader, or simply curious about the intersection of AI and entrepreneurship, I hope this article will provide you with valuable insights.

Understanding the AI-first Approach

The 'AI-first' approach represents a fundamental shift in the way businesses are conceived and built. It signifies the primacy of AI in shaping business models, products, and services, right from the inception of a venture.

In an AI-first company, AI is not an afterthought or a tool to be tacked on later for incremental efficiency gains. Instead, it is an integral part of the company's DNA, influencing every decision, from the problem the company chooses to solve, the product it builds, to the way it interacts with its customers.

Why does this matter? Because AI brings to the table capabilities that were previously unthinkable. With its ability to process and learn from massive amounts of data, AI can uncover patterns, insights, and predictions that can be transformative for businesses. It can automate complex tasks, personalize at scale, and continually adapt and improve over time.

However, to fully harness these benefits, AI must be integrated into the very foundation of a business, and not merely applied as a veneer to existing models. This is what differentiates an AI-first approach from a traditional approach.

AI-first is about creating a business where the core value proposition is deeply intertwined with AI's unique capabilities. This doesn't mean that every problem needs an AI solution, but rather that, given the problem at hand, the solution incorporates AI in a fundamental way from the get-go.

In the next section, we'll explore some practical aspects of implementing an AI-first approach, drawing from my experiences with ArkReach.

Implementing an AI-first Approach: Lessons from ArkReach

At ArkReach, we understood early on that to truly innovate in the field of communication analytics, we needed to take an AI-first approach. This realization has profoundly influenced our journey and the product we have built.

In practical terms, this meant prioritizing AI in our strategic decisions, our product development, and our operations. Here are some of the key lessons we learned along the way:

  1. Start with a clear problem and a hypothesis for how AI can solve it. We noticed that many news media analytics tools relied heavily on social media interaction data, ignoring a treasure trove of online reader behavior data. We hypothesized that AI could help us process this data to provide more nuanced and actionable insights for communication professionals. This clear problem statement and hypothesis guided our product development.
  2. Build a cross-functional team with AI expertise. An AI-first approach requires a mix of skills – data science, engineering, product, and domain expertise. We assembled a team with diverse backgrounds and a shared passion for leveraging AI to transform communication analytics.
  3. Embrace an iterative, learning-oriented process. Developing an AI-first product is not a linear process. It involves building models, testing them, learning from the results, and iterating. This learning-oriented mindset has been critical in our journey.
  4. Prioritize data infrastructure. AI thrives on data. Investing in robust data infrastructure was a priority for us. This allowed us to collect, store, and process vast amounts of data, enabling our AI algorithms to learn and improve.
  5. Think about scale from day one. As we developed ArkReach, we always kept scalability in mind. This influenced decisions around data infrastructure, model selection, and more. By considering scale from the outset, we were able to build a product capable of handling growth without sacrificing performance.
  6. Keep the user at the center. Despite the technological focus, an AI-first approach should never lose sight of the user. We continually sought feedback from our target users, ensuring that our product remained aligned with their needs and preferences.

ArkReach is not an anomaly but a reflection of a broader trend. AI-first companies are proliferating across sectors, from healthcare to finance to education. As per a report by McKinsey, companies that fully absorb AI in their value-creating processes have profit margins 3-15% higher than those of their industry peers[1].

These are still early days in the AI revolution. But the opportunities are immense for those willing to embrace an AI-first approach and navigate the challenges it brings. In the final section, I'll share some thoughts on the future of AI-first entrepreneurship.

The Future of AI-First Entrepreneurship: Opportunities and Challenges

The future of entrepreneurship is AI-first. This statement may sound bold, but it's grounded in reality. A recent survey by Boston Consulting Group and MIT Sloan Management Review found that 90% of respondents view AI as a business opportunity[1]. The adoption of AI is no longer a matter of 'if' but 'when' and 'how'.

As AI continues to evolve, it's opening up new opportunities for entrepreneurs:

  1. Bespoke Solutions: AI's ability to analyze and learn from vast amounts of data means it can provide highly personalized solutions. This opens up opportunities for entrepreneurs to develop AI-first products and services tailored to specific customer needs.
  2. Efficiency Gains: AI can automate many routine tasks, freeing up humans to focus on more strategic, creative work. This can lead to significant efficiency gains, a boon for startups looking to do more with less.
  3. New Business Models: AI is enabling new business models, such as 'AI-as-a-Service', where companies provide AI capabilities as a cloud service. This lowers the barriers to entry for businesses wanting to leverage AI, creating opportunities for AI-first startups.

However, becoming an AI-first entrepreneur is not without its challenges:

  1. Data Privacy: As AI relies on data, issues of data privacy and security are paramount. Entrepreneurs need to navigate these complex issues, ensuring they comply with relevant laws and regulations.
  2. Bias and Fairness: AI models can inadvertently perpetuate biases present in their training data. Entrepreneurs must be aware of this risk and take steps to mitigate it.
  3. Skills Gap: There is a shortage of AI talent, making it challenging for startups to attract and retain the skilled personnel they need.

Despite these challenges, I believe the potential of AI-first entrepreneurship far outweighs the hurdles. As we move into a future where AI is pervasive, entrepreneurs who can effectively leverage AI will be at the forefront of innovation and value creation.

The journey of ArkReach is just one example of the power of an AI-first approach. As an entrepreneur and technologist, I am excited about the possibilities that lie ahead. I invite you to join me in exploring this fascinating frontier.



[1] Stanford University. (2021). Artificial Intelligence Index Report 2021.
[2] McKinsey & Company. (2019). Notes from the AI frontier: Tackling Europe's gap in digital and AI.
[3] McKinsey & Company. (2020). The State of AI in 2020.
[4] Boston Consulting Group and MIT Sloan Management Review. (2021). Expanding AI's Impact with Organizational Learning.

A Quick Troubleshooting Guide to Understanding Potential Reach '<100'

Are you wondering why the potential reach for your article is displaying as less than '<100'? Here are five key reasons why this could be happening and how to address it.

1. Limited content in the media category:

If there aren't enough articles within the same 'media category' on the website where your article is hosted, this could limit the overall audience interest, thereby affecting your potential reach.

2. Incompatible website structure:

Our system may find some website structures incompatible for pulling and analyzing articles. This could also contribute to a lower potential reach number.

3. Historical data access:

Your organization’s Arkreach subscription plan plays a pivotal role in this scenario. The plan should support historical data pull. Sometimes, if the publishing date of the article is beyond the subscribed plan duration (by default, it's a month), the system might not process the article data.

4. Untracked news website domain:

Occasionally, due to technical challenges or non-inclusion in Arkreach’s database, a news website domain might not be tracked. If you face this issue, don't worry. Just email us the website URLs and we'll start tracking them within 24-48 hours.

5. Unique Media list:

Perhaps your work lies in a niche sector, and the news websites that matter to you are quite specialized, regional, or language-specific. Any of the above reasons could be responsible for <100 potential reach.

6. Low reach:

Unfortunately, the reach of the articles might be so low that it falls beneath the detection threshold of our systems.


Facing any of the above issues and want to dig deep?  Here's what you can do:

You can either email us at [email protected] or use the chatbot within the platform. Please include the following information:

  • Copy the dashboard URL from your browser. It would look something like this-
  • The relevant website URLs and article links
  • A brief description of the issue


Modules: Modules refer to the 4 modules offered by Arkreach. These are:

  • Media planning
  • Measurement
  • Crisis & Monitoring
  • Content Optimization

Each module offers state-of-the-art metrics based on reader-centric data.

Audience Persona

Audience Persona:  A specific audience set chosen basis your targeting criteria and share similar characteristics like location, age, gender, income, occupation, education, household size, etc. You can use this to better understand how your targeting selections can limit or expand your targeted audience size. This estimate may vary over time. Tip: You may see improved performance with a broader audience selection.


Media Category

Media Category: It is a specific content category that is used to classify the content publishing/news sites based on the type of content they publish. Examples include health, general news, tech, politics, business, finance, and more. Arkreach supports a wide range of 27 unique media categories to cater to the diverse needs of its users.

Sustained Audience Size

Sustained Audience size: Visitors who take more than one action (e.g. click), and spend quality time consuming the content on the site.

Estimated Audience Size

Estimated Audience size: Largest ‘sustained audience size’ that potentially would have been exposed to within the last 30 days.  Potential reach, on the other hand, is a subset of the Estimated Audience size that pertains to a specific media category.

Potential Reach

Potential reach: The unique potential audience size for this media list category in the last 30 days.